基于非参数Copula-CVaR模型的碳金融市场集成风险测度
|
摘要
我国商业银行等金融机构在参与国际碳金融业务时面临复杂多变的市场环境,其风险评估及预警体系的构建需考虑风险因子的多源性与相依性,对碳金融市场集成风险进行科学测度具有重要意义。本文采用非参数核估计方法确定碳金融市场价格波动与汇率波动两类风险因子的边缘分布,并通过拟合优度检验选择最优Copula函数准确刻画风险因子之间非线性、动态的相依结构,实现对集成条件风险价值CVaR的有效测度。通过Kupiec回测检验及对比各类传统风险测度方法的优劣,发现非参数Copula-CVaR模型能够弥补传统风险测度方法在度量多源风险因子相依性时存在的局限性,避免参数法确定边缘分布时可能出现的模型设定风险与参数估计误差,充分考虑尾部风险,为碳金融市场集成风险测度提供新思路。
In recent years,all countries in the world have focused on tackling climate change and reducing carbon emissions.Currently,it is internationally recognized that the carbon emission trading mechanism is the most effective market mechanism to deal with climate change and control carbon emissions.It can guide funds to the low-carbon industrial chain with the help of price signals.Low-carbon technology will be the strategic commanding point in the future global competition.The development of this industry requires an effective carbon financial capital market to drive a large amount of social capital to low-carbon technology industry.Carbon finance came into being in this context.It is a modern financial innovation that relies on carbon trading,promotes energy conservation and emission reduction by means of financial technology,and serves sustainable economic development.Commercial banks and other financial institutions in China are facing complex and unstable market environments when they participate into international carbon financial market.It is necessary to develop scientific models for measuring the integrated risk of carbon financial market by considering the multiple sources and interdependent relationship of different risk factors.In this paper,the nonparametric kernel estimation method is used to determine the marginal distributions of price risk and exchange risk in carbon financial market.Then,the goodness-of-fit test is used to choose the optimal Copula function that can depict the nonlinear and dynamic dependent structure of risk factors,by which the integrated Conditional Value at Risk(CVaR)is measured effectively.Some findings are drawn by Kupiec back testing and comparison with the conventional risk measurement techniques.The nonparametric Copula-CVaR model can overcome the limitations of the conventional methods in measuring the dependency of multi-source risk factors.When determining the marginal distributions,this approach can avoid model setting risk and parameter estimating error caused by parametric methods.It gives full consideration to tail risk and provides a new way for the measurement of the integrated risk in carbon financial market.The main contributions of the research work are as follows.(1)At the theoretical level,the multi-source risks of the carbon market are clarified and the deficiency of the single risk measurement theory is made up in the existing literature.(2)At the practical level,the accuracy of multisource integration risk measure is improved in the carbon financial market,which provides decision-making reference for the financial institutions to participate in the international carbon finance business.
In recent years,all countries in the world have focused on tackling climate change and reducing carbon emissions.Currently,it is internationally recognized that the carbon emission trading mechanism is the most effective market mechanism to deal with climate change and control carbon emissions.It can guide funds to the low-carbon industrial chain with the help of price signals.Low-carbon technology will be the strategic commanding point in the future global competition.The development of this industry requires an effective carbon financial capital market to drive a large amount of social capital to low-carbon technology industry.Carbon finance came into being in this context.It is a modern financial innovation that relies on carbon trading,promotes energy conservation and emission reduction by means of financial technology,and serves sustainable economic development.Commercial banks and other financial institutions in China are facing complex and unstable market environments when they participate into international carbon financial market.It is necessary to develop scientific models for measuring the integrated risk of carbon financial market by considering the multiple sources and interdependent relationship of different risk factors.In this paper,the nonparametric kernel estimation method is used to determine the marginal distributions of price risk and exchange risk in carbon financial market.Then,the goodness-of-fit test is used to choose the optimal Copula function that can depict the nonlinear and dynamic dependent structure of risk factors,by which the integrated Conditional Value at Risk(CVaR)is measured effectively.Some findings are drawn by Kupiec back testing and comparison with the conventional risk measurement techniques.The nonparametric Copula-CVaR model can overcome the limitations of the conventional methods in measuring the dependency of multi-source risk factors.When determining the marginal distributions,this approach can avoid model setting risk and parameter estimating error caused by parametric methods.It gives full consideration to tail risk and provides a new way for the measurement of the integrated risk in carbon financial market.The main contributions of the research work are as follows.(1)At the theoretical level,the multi-source risks of the carbon market are clarified and the deficiency of the single risk measurement theory is made up in the existing literature.(2)At the practical level,the accuracy of multisource integration risk measure is improved in the carbon financial market,which provides decision-making reference for the financial institutions to participate in the international carbon finance business.
引文
[1]张跃军,魏一鸣.国际碳期货价格的均值回归:基于EU ETS的实证分析[J].系统工程理论与实践,2011,31(2):214-220.
[2]Christiansen A C,Arvanitakis A,Tangen K,et al.Price determinants in the EU emissions trading scheme[J].Climate Policy,2005,5(1):15-30.
[3]Alberola E,Chevallier J.European carbon prices and banking restrictions:Evidence from phase I(2005-2007)[J].The Energy Journal,2009,30(3):51-80.
[4]Blyth W,Bunn D.Coevolution of policy,market and technical price risks in the EU ETS[J].Energy Policy,2011,39(8):4578-4593.
[5]Chevallier J.Carbon futures and macroeconomic risk factors:A view from the EU ETS[J].Energy Economics,2009,31(4):614-625.
[6]Koch N,Fuss S,Grosjean G,et al.Causes of the EUETS price drop:Recession,CDM,renewable policies or a bit of everything?New evidence[J].Energy Policy,2014,73(10):676-685.
[7]Yu J,Mallory M L.Exchange rate effect on carbon credit price via energy markets[J].Journal of International Money&Finance,2014,47(3):145-161.
[8]Zhu Bangzhu,Chevallier J,Ma Shujiao,et al.Examining the structural changes of European carbon futures price 2005-2012[J].Applied Economics Letters,2015,22(5):335-342.
[9]杨超,李国良,门明.国际碳交易市场的风险度量及对我国的启示---基于状态转移与极值理论的VaR比较研究[J].数量经济技术经济研究,2011,28(4):94-109.
[10]Feng Zhenhua,Wei Yiming,Wang Kai.Estimating risk for the carbon market via extreme value theory:An empirical analysis of the EU ETS[J].Applied Energy,2012,99:97-108.
[11]Reboredo J C,Ugando M.Downside risks in EU carbon and fossil fuel markets[J].Mathematics&Computers in Simulation,2015,111:17-35.
[12]蒋晶晶,叶斌,马晓明.基于GARCH-EVT-VaR模型的碳市场风险计量实证研究[J].北京大学学报(自然科学版),2015,51(3):511-517.
[13]Ren Cong,Lo A Y.Emission trading and carbon market performance in Shenzhen,China[J].Applied Energy,2017,193:414-425.
[14]刘维泉,郭兆晖.EU ETS碳排放期货市场风险度量---基于SV模型的实证分析[J].系统工程,2011,29(10):14-23.
[15]唐葆君,申程.碳市场风险及预期收益---欧盟排放贸易体系与清洁发展机制的比较分析[J].北京理工大学学报(社会科学版),2013,15(1):12-18.
[16]张晨,杨玉,张涛.基于Copula模型的商业银行碳金融市场风险整合度量[J].中国管理科学,2015,23(4):61-69.
[17]Chevallier J.Nonparametric modeling of carbon prices[J].Energy Economics,2011,33(6):1267-1282.
[18]黄金波,李仲飞,姚海祥.基于CVaR核估计量的风险管理[J].管理科学学报,2014,17(3):49-59.
[19]黄金波,李仲飞,丁杰.基于非参数核估计方法的均值-VaR模型[J].中国管理科学,2017,25(5):1-10.
[20]张金清,李徐.资产组合的集成风险度量及其应用---基于最优拟合Copula函数的VaR方法[J].系统工程理论与实践,2008,(6):14-21.
[21]李建平,丰吉闯,宋浩,等.风险相关性下的信用风险、市场风险和操作风险集成度量[J].中国管理科学,2010,18(1):18-25.
[22]严太华,韩超.基于极值统计和高维动态C藤Copula的股市行业集成风险计算[J].数理统计与管理,2016,35(6):1098-1108.
[23]Rockafellar R T,Uryasev S.Optimiazation of conditional value-at-risk[J].Journal of Risk,2000,2(3):21-41.
[24]任仙玲,张世英.基于核估计及多元阿基米德Copula的投资组合风险分析[J].管理科学,2007,20(5):92-96.
[25]任仙玲,张世英.基于非参数核密度估计的Copula函数选择原理[J].系统工程学报,2010,25(1):38-44.
[26]张冀,谢远涛,杨娟.风险依赖、一致性风险度量与投资组合---基于Mean-Copula-CVaR的投资组合研究[J].金融研究,2016,(10):159-173.
[27]柏满迎,孙禄杰.三种Copula-VaR计算方法与传统VaR方法的比较[J].数量经济技术经济研究,2007,(2):154-160.
[28]赵鲁涛,李婷,张跃军,等.基于Copula-VaR的能源投资组合价格风险度量研究[J].系统工程理论与实践,2015,35(3):771-779.
[2]Christiansen A C,Arvanitakis A,Tangen K,et al.Price determinants in the EU emissions trading scheme[J].Climate Policy,2005,5(1):15-30.
[3]Alberola E,Chevallier J.European carbon prices and banking restrictions:Evidence from phase I(2005-2007)[J].The Energy Journal,2009,30(3):51-80.
[4]Blyth W,Bunn D.Coevolution of policy,market and technical price risks in the EU ETS[J].Energy Policy,2011,39(8):4578-4593.
[5]Chevallier J.Carbon futures and macroeconomic risk factors:A view from the EU ETS[J].Energy Economics,2009,31(4):614-625.
[6]Koch N,Fuss S,Grosjean G,et al.Causes of the EUETS price drop:Recession,CDM,renewable policies or a bit of everything?New evidence[J].Energy Policy,2014,73(10):676-685.
[7]Yu J,Mallory M L.Exchange rate effect on carbon credit price via energy markets[J].Journal of International Money&Finance,2014,47(3):145-161.
[8]Zhu Bangzhu,Chevallier J,Ma Shujiao,et al.Examining the structural changes of European carbon futures price 2005-2012[J].Applied Economics Letters,2015,22(5):335-342.
[9]杨超,李国良,门明.国际碳交易市场的风险度量及对我国的启示---基于状态转移与极值理论的VaR比较研究[J].数量经济技术经济研究,2011,28(4):94-109.
[10]Feng Zhenhua,Wei Yiming,Wang Kai.Estimating risk for the carbon market via extreme value theory:An empirical analysis of the EU ETS[J].Applied Energy,2012,99:97-108.
[11]Reboredo J C,Ugando M.Downside risks in EU carbon and fossil fuel markets[J].Mathematics&Computers in Simulation,2015,111:17-35.
[12]蒋晶晶,叶斌,马晓明.基于GARCH-EVT-VaR模型的碳市场风险计量实证研究[J].北京大学学报(自然科学版),2015,51(3):511-517.
[13]Ren Cong,Lo A Y.Emission trading and carbon market performance in Shenzhen,China[J].Applied Energy,2017,193:414-425.
[14]刘维泉,郭兆晖.EU ETS碳排放期货市场风险度量---基于SV模型的实证分析[J].系统工程,2011,29(10):14-23.
[15]唐葆君,申程.碳市场风险及预期收益---欧盟排放贸易体系与清洁发展机制的比较分析[J].北京理工大学学报(社会科学版),2013,15(1):12-18.
[16]张晨,杨玉,张涛.基于Copula模型的商业银行碳金融市场风险整合度量[J].中国管理科学,2015,23(4):61-69.
[17]Chevallier J.Nonparametric modeling of carbon prices[J].Energy Economics,2011,33(6):1267-1282.
[18]黄金波,李仲飞,姚海祥.基于CVaR核估计量的风险管理[J].管理科学学报,2014,17(3):49-59.
[19]黄金波,李仲飞,丁杰.基于非参数核估计方法的均值-VaR模型[J].中国管理科学,2017,25(5):1-10.
[20]张金清,李徐.资产组合的集成风险度量及其应用---基于最优拟合Copula函数的VaR方法[J].系统工程理论与实践,2008,(6):14-21.
[21]李建平,丰吉闯,宋浩,等.风险相关性下的信用风险、市场风险和操作风险集成度量[J].中国管理科学,2010,18(1):18-25.
[22]严太华,韩超.基于极值统计和高维动态C藤Copula的股市行业集成风险计算[J].数理统计与管理,2016,35(6):1098-1108.
[23]Rockafellar R T,Uryasev S.Optimiazation of conditional value-at-risk[J].Journal of Risk,2000,2(3):21-41.
[24]任仙玲,张世英.基于核估计及多元阿基米德Copula的投资组合风险分析[J].管理科学,2007,20(5):92-96.
[25]任仙玲,张世英.基于非参数核密度估计的Copula函数选择原理[J].系统工程学报,2010,25(1):38-44.
[26]张冀,谢远涛,杨娟.风险依赖、一致性风险度量与投资组合---基于Mean-Copula-CVaR的投资组合研究[J].金融研究,2016,(10):159-173.
[27]柏满迎,孙禄杰.三种Copula-VaR计算方法与传统VaR方法的比较[J].数量经济技术经济研究,2007,(2):154-160.
[28]赵鲁涛,李婷,张跃军,等.基于Copula-VaR的能源投资组合价格风险度量研究[J].系统工程理论与实践,2015,35(3):771-779.